Assistant Professor

Assistant Professor (Innovation Track) Computer Science / AI / Data Science

Location:

Role Overview

M.Tech freshers

What you will do

• Build & explore:

  Work in your specialization (AI/ML/GenAI, Data Engg, Cloud, IoT, Cybersecurity, etc.) and prototype solutions to

  real problems

  .

• Teach by doing:

  Deliver short, practical sessions on tools and workflows (not just theory).

• Mentor projects:

  Coach student teams from problem statement design MVP demo.

• Extend ideas:

  Take existing mini-projects and evolve them into

  publishable or deployable

  outcomes.

• Run labs & clinics:

  Conduct hands-on labs, code reviews, and weekly office hours.

• Collaborate:

  Support institute initiatives in research, innovation, and entrepreneurship (hackathons, showcases, industry outreach).

Must-have qualifications

• M.Tech

  in CS/IT/AI/ML/Data Science (or closely related field).

• 0–1 year

  experience; strong

  interest in applied research

  and building things.

Technical comfort (nice to have)

• One or more from:

  Python

  ,

  TensorFlow/PyTorch

  ,

  Hugging Face

  ,

  OpenAI APIs

  ,

  LangChain

  ,

  vector DBs

  ,

  FastAPI

  ,

  Docker

  ,

  Cloud (AWS/Azure/GCP)

  ,

  Git/GitHub

  .
• Good grasp of software engineering basics: version control, packaging, testing, deployment.

Soft skills

• Clear communication, structured problem solving, and a

  mentoring mindset

  .
• Ownership, curiosity, and ability to learn new tools quickly.

Employment details

• Type:

  Full-time, on-campus

• Initial commitment:

  Minimum

  60 days

• Notice period:

  1 week

  after completion of 60 days (

  no bond

  )

• Compensation:

  CTC 4.5 LPA

  (per

  annum

  )

• Accommodation:

  On-campus at

  subsidized rates

Growth & outcomes

• High performers

  may be recommended for roles at our product companies:

  Akrivia HCM

  and

  Akrivia Facttwin

  .
• Opportunity to publish, file PoCs, build a public portfolio, and engage with industry partners.

What success looks like (first 60 days)

• Week 1–2: Pick a

  focus area

  ; draft a 6-week mini-syllabus + lab plan.
• Week 3–4: Ship

  1 working prototype

  or

  teaching kit

  (notebook + dataset + README).
• Week 5–6: Mentor

  2–3 student teams

  to functional MVPs; run a demo day.